CollabEdit: Towards Non-Destructive Collaborative Knowledge Editing

Abstract

Recently, collaborative fine-tuning large language models (LLMs) has emerged as a new paradigm for utilizing private data from different parties in a manner that guarantees both efficiency and privacy. Meanwhile, the practical needs of the “right to be forgotten” and the frequent demands to update outdated information, have led to a burgeoning in the techniques of knowledge editing (KE) for LLMs. However, current KE methods are all designed for a single model, and directly adapting current KE methods to collaborative learning scenarios encounters severe performance decreases. In this study, we propose a non-destructive collaborative knowledge editing framework COLLABEDIT that utilizes novel model fusion strategy to preserve overall editing performance. Empirical studies on two canonical datasets demonstrate the effectiveness and superiority of our method compared with other destructive baselines.